MAgNet[CNN]: In this architecture, we follow [1]} and adopt the EDSR architecture [2]} as our CNN encoder. We concatenate all frames \(\lbrace x_t\rbrace _{1\le t \le T}\)  in the channel dimension and feed that to our encoder in order to generate a single representation \(z\) . For the forecasting module, we use the same GNN as in [3]}. A key advantage of this architecture is that it effectively turns existing CNN architectures into mesh-agnostic ones by querying them at any spatially continuous point of the PDE domain at test time.
